TROPPO: tissue-specific reconstruction and phenotype prediction using omics data

Abstract Summary The increasing availability of high-throughput technologies in systems biology has advanced predictive tools like genome-scale metabolic models. Despite this progress, integrating omics data to create accurate, context-specific metabolic models for different tissues or cells remains challenging. A significant issue is that many existing tools rely on proprietary software, which limits accessibility. We introduce TROPPO, an open-source Python library designed to overcome these challenges. TROPPO supports a wide range of context-specific reconstruction algorithms, provides validation methods for assessing generated models, and includes gap-filling algorithms to ensure model consistency, integrating well with other constraint-based tools. Availability and implementation TROPPO is implemented in Python and is freely available at https://github.com/BioSystemsUM/TROPPO and https://pypi.org/project/TROPPO/.

• Publication year

  2025

• Venue

  Bioinformatics Advances

• Publication date

  2025-05-19

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1093/bioadv/vbaf113PMID 40546819PMCID 12179386
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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