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 inBioinformatics (Oxford, England) Vol. 41; no. 7
Main Authors Hovan, Ladislav, Kuijjer, Marieke L
Format Journal Article
LanguageEnglish
Published England Oxford University Press 01.07.2025
Oxford Publishing Limited (England)
Subjects
Online AccessGet full text
ISSN1367-4811
1367-4803
1367-4811
DOI10.1093/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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Snippet Abstract Summary 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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SourceType Open Access Repository
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Publisher
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
URI https://www.ncbi.nlm.nih.gov/pubmed/40576203
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