SPONGE: simple prior omics network GEnerator
Abstract Summary Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription factor binding. There are several tools that utilize this information. However, the prior networks used with them are often not updated and may...
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| Published in | Bioinformatics (Oxford, England) Vol. 41; no. 7 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
01.07.2025
Oxford Publishing Limited (England) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4811 1367-4803 1367-4811 |
| DOI | 10.1093/bioinformatics/btaf320 |
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| Abstract | Abstract
Summary
Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription factor binding. There are several tools that utilize this information. However, the prior networks used with them are often not updated and may fail to reflect the most up-to-date information. Here we present SPONGE, a Python module designed to access information across biological databases, chiefly JASPAR and STRING, to model two types of networks—a prior gene regulatory network mapping transcription factors to genes based on their predicted binding sites, and a prior protein-protein interaction network mapping potential interactions between transcription factors. SPONGE is mainly designed to work with the PANDA algorithm and the corresponding NetZoo family of tools. However, the networks are provided in an easily adaptable format for other tools. SPONGE was designed with ease of use in mind, and it provides sensible default values for all of its parameters while giving the users the freedom to fine-tune them.
Availability and implementation
The code for the Python module and the documentation can be found in our GitHub repository. |
|---|---|
| AbstractList | Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription factor binding. There are several tools that utilize this information. However, the prior networks used with them are often not updated and may fail to reflect the most up-to-date information. Here we present SPONGE, a Python module designed to access information across biological databases, chiefly JASPAR and STRING, to model two types of networks—a prior gene regulatory network mapping transcription factors to genes based on their predicted binding sites, and a prior protein-protein interaction network mapping potential interactions between transcription factors. SPONGE is mainly designed to work with the PANDA algorithm and the corresponding NetZoo family of tools. However, the networks are provided in an easily adaptable format for other tools. SPONGE was designed with ease of use in mind, and it provides sensible default values for all of its parameters while giving the users the freedom to fine-tune them. Availability and implementation The code for the Python module and the documentation can be found in our GitHub repository. Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge on e.g. transcription factor binding. There are several tools that utilise this information. However, the prior networks used with them are often not updated and may fail to reflect the most up-to-date information. Here we present SPONGE, a Python module designed to access information across biological databases, chiefly JASPAR and STRING, to model two types of networks-a prior gene regulatory network mapping transcription factors to genes based on their predicted binding sites, and a prior protein-protein interaction network mapping potential interactions between transcription factors. SPONGE is mainly designed to work with the PANDA algorithm and the corresponding NetZoo family of tools. However, the networks are provided in an easily adaptable format for other tools. SPONGE was designed with ease of use in mind, and it provides sensible default values for all of its parameters while giving the users freedom to fine-tune them. The code for the Python module and the documentation can be found in our GitHub repository.Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge on e.g. transcription factor binding. There are several tools that utilise this information. However, the prior networks used with them are often not updated and may fail to reflect the most up-to-date information. Here we present SPONGE, a Python module designed to access information across biological databases, chiefly JASPAR and STRING, to model two types of networks-a prior gene regulatory network mapping transcription factors to genes based on their predicted binding sites, and a prior protein-protein interaction network mapping potential interactions between transcription factors. SPONGE is mainly designed to work with the PANDA algorithm and the corresponding NetZoo family of tools. However, the networks are provided in an easily adaptable format for other tools. SPONGE was designed with ease of use in mind, and it provides sensible default values for all of its parameters while giving the users freedom to fine-tune them. The code for the Python module and the documentation can be found in our GitHub repository. Abstract Summary Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription factor binding. There are several tools that utilize this information. However, the prior networks used with them are often not updated and may fail to reflect the most up-to-date information. Here we present SPONGE, a Python module designed to access information across biological databases, chiefly JASPAR and STRING, to model two types of networks—a prior gene regulatory network mapping transcription factors to genes based on their predicted binding sites, and a prior protein-protein interaction network mapping potential interactions between transcription factors. SPONGE is mainly designed to work with the PANDA algorithm and the corresponding NetZoo family of tools. However, the networks are provided in an easily adaptable format for other tools. SPONGE was designed with ease of use in mind, and it provides sensible default values for all of its parameters while giving the users the freedom to fine-tune them. Availability and implementation The code for the Python module and the documentation can be found in our GitHub repository. Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription factor binding. There are several tools that utilize this information. However, the prior networks used with them are often not updated and may fail to reflect the most up-to-date information. Here we present SPONGE, a Python module designed to access information across biological databases, chiefly JASPAR and STRING, to model two types of networks-a prior gene regulatory network mapping transcription factors to genes based on their predicted binding sites, and a prior protein-protein interaction network mapping potential interactions between transcription factors. SPONGE is mainly designed to work with the PANDA algorithm and the corresponding NetZoo family of tools. However, the networks are provided in an easily adaptable format for other tools. SPONGE was designed with ease of use in mind, and it provides sensible default values for all of its parameters while giving the users the freedom to fine-tune them. The code for the Python module and the documentation can be found in our GitHub repository. |
| Author | Kuijjer, Marieke L Hovan, Ladislav |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40576203$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1093/bioadv/vbae099 10.1093/bioinformatics/btr064 10.1093/g3journal/jkad004 10.1016/j.celrep.2017.10.001 10.1371/journal.pone.0064832 10.1093/bioinformatics/btw422 10.1093/nar/gkac1052 10.1186/s13059-023-02877-1 10.1016/j.molcel.2022.12.032 10.1093/nar/gkac1000 10.1371/journal.pcbi.1003666 10.1038/s41597-024-03571-y 10.1093/nar/gkad1049 10.1093/nar/gkad1059 10.1093/nar/gkab1113 10.1038/s41421-018-0045-y 10.7554/eLife.04837 10.1093/narcan/zcac008 |
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Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription... Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge, e.g. transcription factor binding.... Gene regulatory networks modelled from experimental data can be improved through the use of prior biological knowledge on e.g. transcription factor binding.... |
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| SubjectTerms | Algorithms Binding Sites Computational Biology - methods Databases, Genetic Gene mapping Gene Regulatory Networks Information processing Mapping Modules Networks Peptide mapping Protein interaction Protein Interaction Mapping - methods Proteins Software Transcription factors Transcription Factors - metabolism |
| Title | SPONGE: simple prior omics network GEnerator |
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