SIGNET : transcriptome-wide causal inference for gene regulatory networks
© 2023. The Author(s)..
Gene regulation plays an important role in understanding the mechanisms of human biology and diseases. However, inferring causal relationships between all genes is challenging due to the large number of genes in the transcriptome. Here, we present SIGNET (Statistical Inference on Gene Regulatory Networks), a flexible software package that reveals networks of causal regulation between genes built upon large-scale transcriptomic and genotypic data at the population level. Like Mendelian randomization, SIGNET uses genotypic variants as natural instrumental variables to establish such causal relationships but constructs a transcriptome-wide gene regulatory network with high confidence. SIGNET makes such a computationally heavy task feasible by deploying a well-designed statistical algorithm over a parallel computing environment. It also provides a user-friendly interface allowing for parameter tuning, efficient parallel computing scheduling, interactive network visualization, and confirmatory results retrieval. The Open source SIGNET software is freely available ( https://www.zstats.org/signet/ ).
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
Scientific reports - 13(2023), 1 vom: 08. Nov., Seite 19371 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jiang, Zhongli [VerfasserIn] |
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Date Completed 09.11.2023 Date Revised 10.02.2024 published: Electronic UpdateOf: Res Sq. 2023 Jul 26;:. - PMID 37546848 Citation Status MEDLINE |
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| doi: |
10.1038/s41598-023-46295-6 |
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| funding: |
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NLM364303395 |
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| 520 | |a Gene regulation plays an important role in understanding the mechanisms of human biology and diseases. However, inferring causal relationships between all genes is challenging due to the large number of genes in the transcriptome. Here, we present SIGNET (Statistical Inference on Gene Regulatory Networks), a flexible software package that reveals networks of causal regulation between genes built upon large-scale transcriptomic and genotypic data at the population level. Like Mendelian randomization, SIGNET uses genotypic variants as natural instrumental variables to establish such causal relationships but constructs a transcriptome-wide gene regulatory network with high confidence. SIGNET makes such a computationally heavy task feasible by deploying a well-designed statistical algorithm over a parallel computing environment. It also provides a user-friendly interface allowing for parameter tuning, efficient parallel computing scheduling, interactive network visualization, and confirmatory results retrieval. The Open source SIGNET software is freely available ( https://www.zstats.org/signet/ ) | ||
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| 700 | 1 | |a Xu, Zhenyu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaojian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Dabao |e verfasserin |4 aut | |
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