shinyseg:用于灵活共聚和敏感性分析的网络应用程序。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2024-04-10 DOI:10.1093/bioinformatics/btae201
Christian Carrizosa, Dag E Undlien, Magnus D Vigeland
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引用次数: 0

摘要

动机osegregation 分析是鉴定致病基因变异的强大工具,但其实施仍具有挑战性。现有软件要么范围有限,要么对许多最终用户来说要求过高。此外,目前的解决方案缺乏评估共聚集证据稳健性的方法,而这一点由于共聚集依赖于不确定的估计值而非常重要。我们的应用程序根据责任类别或流行病学数据(如风险、危险比和发病年龄分布)简化了穿透性规范。此外,它还结合了敏感性分析,以评估共聚集证据的稳健性,并为临床解释提供支持。可用性和实施方法可在 https://chrcarrizosa.shinyapps.io/shinyseg 免费获取 shinyseg 应用程序,文档和完整的 R 源代码可在 https://chrcarrizosa.github.io/shinyseg 和 https://github.com/chrcarrizosa/shinyseg.SUPPLEMENTARY 获取信息补充数据可在 Bioinformatics online 上获取。
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shinyseg: a web application for flexible cosegregation and sensitivity analysis.
MOTIVATION Cosegregation analysis is a powerful tool for identifying pathogenic genetic variants, but its implementation remains challenging. Existing software is either limited in scope or too demanding for many end users. Moreover, current solutions lack methods for assessing the robustness of cosegregation evidence, which is important due to its reliance on uncertain estimates. RESULTS We present shinyseg, a comprehensive web application for clinical cosegregation analysis. Our app streamlines penetrance specification based on either liability classes or epidemiological data such as risks, hazard ratios, and age of onset distribution. In addition, it incorporates sensitivity analyses to assess the robustness of cosegregation evidence, and offers support in clinical interpretation. AVAILABILITY AND IMPLEMENTATION The shinyseg app is freely available at https://chrcarrizosa.shinyapps.io/shinyseg, with documentation and complete R source code on https://chrcarrizosa.github.io/shinyseg and https://github.com/chrcarrizosa/shinyseg. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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