Executable Network Models of Integrated Multiomics Data.

Executable Network Models of Integrated Multiomics Data.
复制标题

DOI:
10.1021/acs.jproteome.2c00730
复制
发表时间:
2023-05-05
影响因子:
4.4
通讯作者:
Thakar, Juilee
Thakar, Juilee
中科院分区:
生物学2区
文献类型:
--
作者:
Palshikar, Mukta G.;Min, Xiaojun;Crystal, Alexander;Meng, Jiayue;Hilchey, Shannon P.;Zand, Martin S.;Thakar, Juilee

文献摘要

参考文献

相似文献

多组学分析提供了一个正在检查的条件的整体图片,并捕捉信号事件的复杂性,从原始原因(环境或遗传)开始,到多个分子层的下游功能变化。通路富集分析已与多组学数据集一起用于表征信号传导机制。然而,这些分层数据之间的技术和生物可变性限制了综合计算分析。我们提出了一种基于布尔网络的方法,多组学布尔组学网络不变时间分析(mBONITA),以整合组学数据集,量化多个分子层。mBONITA利用先验知识网络进行基于拓扑的途径分析。此外,mBONITA通过将观察到的倍数变化和方差与节点测量(即,基因或蛋白质)对信号传导的影响,以及跨数据集对该基因的证据强度的测量。我们使用mBONITA来整合拉莫斯B细胞的多组学数据集,这些细胞在不同的O2压力下用免疫抑制剂环孢素A处理,以确定参与低氧介导的趋化性的途径。我们比较了mBONITA的性能与其他6个多组学数据设计的途径分析方法,并表明mBONITA确定了一组途径,在所有组学层的调制证据。mBONITA可在以下网址免费获得。
Multiomics profiling provides a holistic picture of a condition being examined and captures the complexity of signaling events, beginning from the original cause (environmental or genetic), to downstream functional changes at multiple molecular layers. Pathway enrichment analysis has been used with multiomics data sets to characterize signaling mechanisms. However, technical and biological variability between these layered data limit an integrative computational analyses. We present a Boolean network-based method, multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), to integrate omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform topology-based pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular measurements by combining observed fold-changes and variance, with a measure of node (i.e., gene or protein) influence over signaling, and a measure of the strength of evidence for that gene across data sets. We used mBONITA to integrate multiomics data sets from RAMOS B cells treated with the immunosuppressant drug cyclosporine A under varying O2 tensions to identify pathways involved in hypoxia-mediated chemotaxis. We compare mBONITA’s performance with 6 other pathway analysis methods designed for multiomics data and show that mBONITA identifies a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at .
DOI: 10.1093/bioinformatics/btw313
发表时间: 2016-09-15
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Gu, Zuguang;Eils, Roland;Schlesner, Matthias
通讯作者: Schlesner, Matthias
DOI: 10.4161/cib.27634
发表时间: 2013-11-01
影响因子: --
作者:
Bogdan S;Schultz J;Grosshans J
通讯作者: Grosshans J
DOI: 10.1038/ncomms13509
发表时间: 2016-11-16
影响因子: 16.6
作者:
Damiani D;Goffinet AM;Alberts A;Tissir F
通讯作者: Tissir F
DOI: 10.1016/j.cell.2013.06.037
发表时间: 2013-08-01
期刊: CELL
影响因子: 64.5
作者:
De Bock, Katrien;Georgiadou, Maria;Carmeliet, Peter
通讯作者: Carmeliet, Peter
DOI: 10.3389/fnmol.2011.00007
发表时间: 2011
影响因子: 4.8
作者:
Gorospe M;Tominaga K;Wu X;Fähling M;Ivan M
通讯作者: Ivan M