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基因组学中的亚组分析、鉴定和可视化。

Analysis, identification and visualization of subgroups in genomics.

机构信息

Institute of Medical Systems Biology (MSB), Ulm University, Ulm, Germany.

Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Germany.

出版信息

Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa217.

DOI:10.1093/bib/bbaa217
PMID:32954413
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8138884/
Abstract

MOTIVATION

Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients' samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches.

RESULTS

Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar ('analysis and visualization of alteration data') that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection.

AVAILABILITY

AVAtar is available at: https://github.com/sysbio-bioinf/avatar.

CONTACT

hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502.

摘要

动机

癌症是一种复杂的异质疾病，涉及多个体细胞突变，这些突变在其进展过程中积累。在过去的几年中，患者样本的基因组数据的广泛可用性为分析基因突变和改变开辟了新的视角。因此，可视化和进一步鉴定大量患者中发生突变的基因是当今的一项关键任务，这为更个性化的干预方法提供了线索。

结果

在这里，我们广泛回顾了用于可视化和分析改变数据的现有工具。我们比较了研究大规模组学数据中互斥性和样本覆盖的不同方法。我们通过独立的软件 AVAtar（“改变数据的分析和可视化”）来补充我们的评论，该软件将来自不同工具的不同方面整合到一个综合平台中。AVAtar 通过多目标进化算法为自定义改变图补充子集识别，并为并发解决方案的评估提供创新且用户友好的界面。来自个性化医学的一个用例展示了其独特的功能，展示了在疫苗靶标选择中的应用。

可用性

AVAtar 可在以下网址获得：https://github.com/sysbio-bioinf/avatar。

联系方式

hans.kestler@uni-ulm.de，电话：+49 (0) 731 500 24 500，传真：+49 (0) 731 500 24 502。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/797629e982b9/bbaa217f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/9d55f49ef7a7/bbaa217f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/5fc9eab2e55d/bbaa217f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/1d258b1c8629/bbaa217f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/36dc082d4ae3/bbaa217f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/ec00c5246896/bbaa217f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/64f880c62e16/bbaa217f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/797629e982b9/bbaa217f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/9d55f49ef7a7/bbaa217f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/5fc9eab2e55d/bbaa217f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/1d258b1c8629/bbaa217f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/36dc082d4ae3/bbaa217f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/ec00c5246896/bbaa217f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/64f880c62e16/bbaa217f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da4a/8138884/797629e982b9/bbaa217f7.jpg

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