A python library for the fast and scalable computation of biologically meaningful individual specific networks.
Biological networks
Individual specific networks
Network analysis
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 Aug 2024
06 Aug 2024
Historique:
received:
26
02
2024
accepted:
31
07
2024
medline:
7
8
2024
pubmed:
7
8
2024
entrez:
6
8
2024
Statut:
epublish
Résumé
Individual Specific Networks (ISNs) are a tool used in computational biology to infer Individual Specific relationships between biological entities from omics data. ISNs provide insights into how the interactions among these entities affect their respective functions. To address the scarcity of solutions for efficiently computing ISNs on large biological datasets, we present ISN-tractor, a data-agnostic, highly optimized Python library to build and analyse ISNs. ISN-tractor demonstrates superior scalability and efficiency in generating Individual Specific Networks (ISNs) when compared to existing methods such as LionessR, both in terms of time and memory usage, allowing ISNs to be used on large datasets. We show how ISN-tractor can be applied to real-life datasets, including The Cancer Genome Atlas (TCGA) and HapMap, showcasing its versatility. ISN-tractor can be used to build ISNs from various -omics data types, including transcriptomics, proteomics, and genotype arrays, and can detect distinct patterns of gene interactions within and across cancer types. We also show how Filtration Curves provided valuable insights into ISN characteristics, revealing topological distinctions among individuals with different clinical outcomes. Additionally, ISN-tractor can effectively cluster populations based on genetic relationships, as demonstrated with Principal Component Analysis on HapMap data.
Identifiants
pubmed: 39107347
doi: 10.1038/s41598-024-69067-2
pii: 10.1038/s41598-024-69067-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
18243Subventions
Organisme : H2020 Marie Sklodowska-Curie grant agreement (TranSYS)
ID : 860895
Organisme : FWO senior post-doctoral fellowship
ID : 12Y5623N
Informations de copyright
© 2024. The Author(s).
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