Joint embedding of biological networks for cross-species functional alignment.

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad529
Lechuan Li, Ruth Dannenfelser, Yu Zhu, Nathaniel Hejduk, Santiago Segarra, Vicky Yao
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Abstract

Motivation: Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.

Results: We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA's embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.

Availability and implementation: https://github.com/ylaboratory/ETNA.

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用于跨物种功能比对的生物网络的联合嵌入。
动机:模式生物被广泛用于更好地了解人类疾病的分子原因。虽然序列相似性极大地帮助了这种跨物种转移,但序列相似性并不意味着功能相似,因此,目前的几种方法结合了蛋白质-蛋白质相互作用,以帮助绘制物种之间的发现图。现有的传输方法要么将对齐问题表述为使网络特征与已知的正交性相匹配的匹配问题,要么最近将其表述为联合嵌入问题。结果:我们提出了一种新的最先进的联合嵌入解决方案:嵌入到网络对齐(ETNA)。ETNA基于网络拓扑结构生成单独的网络嵌入,然后使用受自然语言处理启发的交叉训练方法,使用基于序列的正交对数对两个嵌入进行对齐。最终的嵌入保留了物种内部和物种之间的基因功能关系,我们证明它捕获了成对和群体功能相关性。此外,ETNA的嵌入物可用于跨物种转移遗传相互作用并确定表型比对,为药物再利用和转化研究的潜在机会奠定基础。可用性和实施:https://github.com/ylaboratory/ETNA.
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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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