Integrative Gene Expression and Metabolic Analysis Tool
Cobra Toolbox 3.0
MATLAB
flux balance analysis
flux variability analysis
genome-scale metabolic modeling
omics data analysis
software engineering
transcriptomics
Journal
Biomolecules
ISSN: 2218-273X
Titre abrégé: Biomolecules
Pays: Switzerland
ID NLM: 101596414
Informations de publication
Date de publication:
16 04 2022
16 04 2022
Historique:
received:
17
03
2022
revised:
11
04
2022
accepted:
14
04
2022
entrez:
23
4
2022
pubmed:
24
4
2022
medline:
27
4
2022
Statut:
epublish
Résumé
Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype-phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool
Identifiants
pubmed: 35454176
pii: biom12040586
doi: 10.3390/biom12040586
pmc: PMC9029533
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
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