ArticleProceedings of the National Academy of Sciences of the United States of America2017
Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 38 citations in OpenAlex.
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- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
- Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data.Genome biology · 2024Article
- Systematic analysis of Mendelian disease-associated gene variants reveals new classes of cancer-predisposing genes.Genome medicine · 2023Article
- Genome-scale metabolic models applied for human health and biopharmaceutical engineering.Quantitative biology (Beijing, China) · 2023Review
- Network medicine: an approach to complex kidney disease phenotypes.Nature reviews. Nephrology · 2023Review
- Proteomic analysis for the effects of non-saponin fraction with rich polysaccharide from Korean Red Ginseng on Alzheimer's disease in a mouse model.Journal of ginseng research · 2023Article
- Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.Metabolites · 2023Review
- Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer.PloS one · 2023Article
- Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.Cells · 2022Review
- A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models.Computational and structural biotechnology journal · 2022Article
- Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells.Frontiers in genetics · 2021Review
- An atlas of human metabolism.Science signaling · 2020Article
- Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues.Royal Society open science · 2020Article
- Compartment and hub definitions tune metabolic networks for metabolomic interpretations.GigaScience · 2020Article
- The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020Review
- Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Current status and applications of genome-scale metabolic models.Genome biology · 2019Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism. The framework in this study generated 11,415 GeTPRA corresponding to 1,106 metabolic genes for both principal and nonprincipal transcripts (PTs and NPTs) of metabolic genes. The framework further evaluates GeTPRA, using a human genome-scale metabolic model (GEM) that is biochemically consistent and transcript-level data compatible, and subsequently updates the human GEM. A generic human GEM, Recon 2M.1, was developed for this purpose, and subsequently updated to Recon 2M.2 through the framework. Both PTs and NPTs of metabolic genes were considered in the framework based on prior analyses of 446 personal RNA-Seq data and 1,784 personal GEMs reconstructed using Recon 2M.1. The framework and the GeTPRA will contribute to better understanding human metabolism at the systems level and enable further medical applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.